loss_weighted_crossentropy.py

import tensorflow as tf
from keras import backend as K
import numpy as np

def weighted_categorical_crossentropy(weights):
    # https://forums.fast.ai/t/unbalanced-classes-in-image-segmentation/18289
    """ weighted_categorical_crossentropy

        Args:
            * weights<ktensor|nparray|list>: crossentropy weights
        Returns:
            * weighted categorical crossentropy function
    """
    if isinstance(weights,list) or isinstance(np.ndarray):
        weights=K.variable(weights)

    def loss(target,output,from_logits=False):
        if not from_logits:
            output /= tf.reduce_sum(output,
                                    len(output.get_shape()) - 1,
                                    True)
            _epsilon = tf.convert_to_tensor(K.epsilon(), dtype=output.dtype.base_dtype)
            output = tf.clip_by_value(output, _epsilon, 1. - _epsilon)
            losses = target * tf.log(output)
            print(losses)
            weighted_losses = target * tf.log(output) * weights
            print(weighted_losses)
            return - tf.reduce_sum(weighted_losses,len(output.get_shape()) - 1)
        else:
            raise ValueError('WeightedCategoricalCrossentropy: not valid with logits')
    return loss